Flexible Job Shop Dynamic Scheduling and Fault Maintenance Personnel Cooperative Scheduling Optimization Based on the ACODDQN Algorithm

• Published in: Mathematics (Basel) Vol. 13; no. 6; p. 932
• Main Authors: Lu, Jiansha, Zhang, Jiarui, Cao, Jun, Xu, Xuesong, Shao, Yiping, Cheng, Zhenbo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: ACODDQN algorithm; Algorithms; Ant colony optimization; Automation; Completion time; Deep learning; Delay time; Emergency equipment; Energy consumption; equipment fault diagnosis and maintenance; Failure; Fault diagnosis; Genetic algorithms; global search capabilities; Heuristic; Job shops; Machine learning; Maintenance; Manufacturing; multi-resource and multi-objective model; Multiple objective analysis; Objectives; Optimization; Optimization models; Personnel; Production management; Production scheduling; Resource scheduling; Scheduling; Solution space; Workpieces
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math13060932

In order to address the impact of equipment fault diagnosis and repair delays on production schedule execution in the dynamic scheduling of flexible job shops, this paper proposes a multi-resource, multi-objective dynamic scheduling optimization model, which aims to minimize delay time and completion time. It integrates the scheduling of the workpieces, machines, and maintenance personnel to improve the response efficiency of emergency equipment maintenance. To this end, a self-learning Ant Colony Algorithm based on deep reinforcement learning (ACODDQN) is designed in this paper. The algorithm searches the solution space by using the ACO, prioritizes the solutions by combining the non-dominated sorting strategies, and achieves the adaptive optimization of scheduling decisions by utilizing the organic integration of the pheromone update mechanism and the DDQN framework. Further, the generated solutions are locally adjusted via the feasible solution optimization strategy to ensure that the solutions satisfy all the constraints and ultimately generate a Pareto optimal solution set with high quality. Simulation results based on standard examples and real cases show that the ACODDQN algorithm exhibits significant optimization effects in several tests, which verifies its superiority and practical application potential in dynamic scheduling problems.